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@@ -984,7 +984,7 @@ function srcnn.cunet_v4(backend, ch)
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return model
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end
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-function srcnn.cunet_v5(backend, ch)
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+function srcnn.cunet_v6(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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@@ -1000,22 +1000,37 @@ function srcnn.cunet_v5(backend, ch)
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model:add(nn.CAddTable())
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return model
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end
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- function unet_conv(n_input, n_middle, n_output)
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+ function unet_conv(n_input, n_middle, n_output, se)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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+ if se then
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+ -- Squeeze and Excitation Networks
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+ local con = nn.ConcatTable(2)
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+ local attention = nn.Sequential()
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+ attention:add(nn.SpatialAdaptiveAveragePooling(1, 1)) -- global average pooling
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+ attention:add(SpatialConvolution(backend, n_output, math.floor(n_output / 4), 1, 1, 1, 1, 0, 0))
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+ attention:add(nn.ReLU(true))
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+ attention:add(SpatialConvolution(backend, math.floor(n_output / 4), n_output, 1, 1, 1, 1, 0, 0))
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+ attention:add(nn.Sigmoid(true))
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+ con:add(nn.Identity())
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+ con:add(attention)
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+ model:add(con)
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+ model:add(w2nn.ScaleTable())
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+ end
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return model
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end
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+ -- Residual U-Net
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function unet(backend, ch, deconv)
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- local block1 = unet_conv(128, 256, 128)
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+ local block1 = unet_conv(128, 256, 128, true)
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local block2 = nn.Sequential()
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- block2:add(unet_conv(64, 64, 128))
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+ block2:add(unet_conv(64, 64, 128, true))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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- block2:add(unet_conv(128, 64, 64))
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+ block2:add(unet_conv(128, 64, 64, true))
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local model = nn.Sequential()
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- model:add(unet_conv(ch, 32, 64))
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+ model:add(unet_conv(ch, 32, 64, false))
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model:add(unet_branch(block2, backend, 64, 64, 16))
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model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1))
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@@ -1042,7 +1057,7 @@ function srcnn.cunet_v5(backend, ch)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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- model.w2nn_arch_name = "cunet_v5"
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+ model.w2nn_arch_name = "cunet_v6"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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@@ -1186,13 +1201,13 @@ print(model)
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model:training()
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print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()):size())
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os.exit()
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-
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-local model = srcnn.cunet_v5("cunn", 3):cuda()
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+local model = srcnn.cunet_v6("cunn", 3):cuda()
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print(model)
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model:training()
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print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()))
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os.exit()
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+
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--]]
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return srcnn
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nagadomi